Developing a Data Strategy for Your Institution: Steps for Best Practice

In today’s rapidly changing higher education landscape, making decisions grounded in accurate, timely data is essential. Competition for enrollment, scrutiny of outcomes and student ROI, budget constraints, regulatory requirements, and other factors are all reasons trustworthy data is needed. Creating an environment that students will choose—and in which they will succeed—requires insight.

Developing a formal data strategy is one of the most powerful steps an institution can take to ensure that data are intentionally collected, responsibly governed, securely stored, and meaningfully used. A well-articulated institutional data strategy aligns strategic plan priorities with data practices. The goals of a data strategy are to clarify responsibilities and build a strong foundation for analytics and decision support.

In building a sustainable data strategy, it is important to consider the following steps.

Step One: Secure Executive Leadership Backing

This may seem obvious; however, without a directive or strong backing from executive leadership, developing an institutional data strategy is an effort destined to struggle. Developing a data strategy is a transformational institutional effort, not just an IT/IR initiative.

In addition to providing a clear directive, the role of executive leadership in this effort is to help others in the institution understand how this is critical to advancing priorities and goals, allocating resources, and reinforcing expectations for participation and accountability. Executive leadership provides more than just a figurehead for the data strategy initiative, it communicates that data-informed decision making is an institutional priority.

Step Two: Create a Representative Planning Committee

In constituting a planning committee, it is important to select members in consultation with executive leadership. The committee’s charge will be to lead the efforts in developing an ideal state of the data, assessing the current state, and building a roadmap to bridge the gap between the two.

If possible, choose people who understand change management and the importance of buy-in for a data strategy. Careful and representative selection of members can help to alleviate skepticism that exists in any transformational initiative. Keep the committee to a manageable size and work to get representation in the following roles:

Executive Leadership: Provides direction and ensures alignment with institutional priorities
Data Stewards: Defines data elements and ensuring data integrity
Data Managers: Responsible for system-level data management
Data Entry Personnel: Consult around frontline data capture challenges
Subject Matter Experts (SMEs): Consult from the perspectives of enrollment, academic affairs, finance, student services, advancement, etc.

Step Three: Imagine the “Ideal State” of Data

Before assessing current challenges, the institution must define what the “ideal state of the data” looks like. The goal is to describe how data would function in the best-case scenario.

Can leadership access dashboards as they expect?
Are definitions standardized across the institution?
Is there a clear data governance structure and is it well documented?
Are data entry processes documented and consistent?
Is data literacy embedded across the institution?
Are privacy and security protections clearly defined?

The ideal state should include both technical and cultural components and should serve to address technical advancements as well as transform how the institution views and uses the data.

Documenting the ideal state provides:

A shared vision
A benchmark for assessment
A north star for decision-making

A major benefit of striving to attain the ideal state is to move an institutional culture from reactive to proactive and strategic when it comes to decision-making. For example, moving enrollment planning from reacting to low enrollments to planning for maximizing or right-sizing enrollments.

Step Four: Assess the Current State

Once the ideal state is defined, the next step is to conduct a thorough assessment of the institution’s current data environment.

This assessment may include:

Maturity of the data environment
Data governance structure
Data quality and consistency
Reporting tools and capabilities
System integrations
Data definitions and documentation
Security and compliance practices
Staff capacity and data literacy

Methods may include maturity assessments, surveys, interviews, system audits, and documentation reviews. Several third-party tools are available to help guide institutions, particularly if this is the inaugural development of a data strategy.

Keep in mind that the goal is a strategic plan to improve the data and better align with institutional priorities, not to assign blame. The assessment will help to identify:

Gaps between current and ideal states
Redundancies and inefficiencies
Risks (compliance, reputational, operational)
Quick wins and long-term challenges
Unclear ownership of data
Siloed environments
Inconsistent definitions

Assessments need to be transparent and honest if the institution plans to use the results to build the roadmap and ground it in reality.

Step Five: Build a Roadmap Toward the Ideal State

With weaknesses identified, the planning committee can begin to develop a strong, phased roadmap.

A strong roadmap:

Prioritizes initiatives based on institutional priorities.
Weighs the impacts of quick wins with long-term goals.
Assigns ownership and accountability.
Includes measurable outcomes.
Aligns with budget and resource constraints.

Consider developing/improving these components for a roadmap:

Establishing a formal data governance structure
Creating standardized data definitions and documentation
Improving system integrations
Implementing transparent metrics for data management efforts (i.e., data modeling, architecture, dashboard usage, etc.)
Investing in analytics
Providing data literacy training across campus

The roadmap should span multiple years, ideally mapped to the strategic plan, and be realistic. Remember that prioritization is important to avoid fatigue. This is a continuous process and not everything can be done all at once. Once developed, it is important to transparently communicate the roadmap across the institution.

Step Six: Continuous Assessment and Improvement

As with most things in higher education, a data strategy cannot be static. It is subject to changes in priorities, technological advancements, regulations, etc. and must continuously adapt.

Best practices for sustainability include:

Assessing data governance structures
Monitoring data quality metrics
Updating documentation
Conducting assessments, including maturity assessments to monitor maturation and growth
Celebrating movement towards a culture of data excellence

Engaging in continuous improvement of the data strategy assures sustainability and communicates the institution’s commitment to best practices and excellence in data management.

Conclusion

Data in higher education has not always been viewed as an asset but often as an operational byproduct. Data is too often created solely for processing, an afterthought— getting students into seats and paying employees—rather than created to also inform. Institutions that are strategic in the planning of the data lifecycle are best positioned to make informed decisions, steward resources efficiently and effectively, inform intervention strategies for student outcomes, and demonstrate transparency and accountability.

The payoff for investing in developing a formal data strategy is substantial. Accurate, trustworthy, and time-relevant data can transform an institution’s decisions and create a culture where data are not only trusted and available, but actionable as well.

References

Carruthers, C., & Jackson, P. (2020). The chief data officer's playbook (2nd ed.). Facet Publishing.

DAMA International. (2024). DAMA-DMBOK: Data management body of knowledge (2nd ed, revised). Technics Publications, LLC.

Gartner. Key success factors in any data and analytics strategy. Retrieved February 26, 2026, from https://www.gartner.com/en/data-analytics/topics/data-analytics-strategy.

Harvard Business Review. (2025). HBR's 10 must reads on data strategy (featuring "Democratizing Transformation" by Marco Iansiti and Satya Nadella). Harvard Business Review Press.

IBM. Design your data strategy in six steps. Retrieved February 26, 2026, from https://www.ibm.com/think/insights/data-matters/data-strategy.

Dr. Sonia Schaible Brandon is an accomplished higher education leader with more than two decades of experience in institutional research, academic effectiveness, and data-informed decision making. Currently serving as the Director of Institutional Research and Effectiveness at the University of Northern Colorado, she has successfully established centralized data infrastructures for decision support, implemented institutional data governance frameworks, developed data strategy, and led initiatives advancing equity, student success, and organizational efficiency.